Lossless fitness inheritance in genetic algorithms for decision trees

Name: Lossless fitness inheritance in genetic algorithms for decision trees
Keywords: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Computer Science - Neural and Evolutionary Computing, Data Structures and Algorithms (cs.DS), Neural and Evolutionary Computing (cs.NE)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2006Embargo end date: 01 Jan 2006Publisher:arXiv

Authors: Kalles, Dimitris; Papagelis, Athanassios;

doi: 10.48550/arxiv.cs/0611166

arXiv: cs/0611166

Lossless fitness inheritance in genetic algorithms for decision trees

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Abstract

When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is enough for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speed-up on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.

Contains 23 pages, 6 figures, 12 tables. Text last updated as of March 6, 2009. Submitted to a journal

Keywords

FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Computer Science - Neural and Evolutionary Computing, Data Structures and Algorithms (cs.DS), Neural and Evolutionary Computing (cs.NE)

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